The present application relates to the conversion of graphics data formats from one form to another, and specifically to the conversion of (red-green-blue) RGB graphics to improved color pixel arrangements used in displays.
The present state of the art of color single plane imaging matrix, for flat panel displays, use the RGB color triad or a single color in a vertical stripe as shown in prior art FIG. 1. The system takes advantage of the Von Bezold color blending effect (explained further herein) by separating the three colors and placing equal spatial frequency weight on each color. However, these panels are a poor match to human vision.
Graphic rendering techniques have been developed to improve the image quality of prior art panels. Benzschawel, et al. in U.S. Pat. No. 5,341,153 teach how to reduce an image of a larger size down to a smaller panel. In so doing, Benzschawel, et al. teach how to improve the image quality using a technique now known in the art as “sub-pixel rendering”. More recently Hill, et al. in U.S. Pat. No. 6,188,385 teach how to improve text quality by reducing a virtual image of text, one character at a time, using the very same sub-pixel rendering technique.
The above prior art pay inadequate attention to how human vision operates. The prior art's reconstruction of the image by the display device is poorly matched to human vision.
The dominant model used in sampling, or generating, and then storing the image for these displays is the RGB pixel (or three-color pixel element), in which the red, green, and blue values are on an orthogonal equal spatial resolution grid and are co-incident. One of the consequences of using this image format is that it is a poor match both to the real image reconstruction panel, with its spaced apart, non-coincident, color emitters, and to human vision. This effectively results in redundant, or wasted, information in the image.
Martinez-Uriegas, et al. in U.S. Pat. No. 5,398,066 and Peters, et al. in U.S. Pat. No. 5,541,653 teach a technique to convert and store images from RGB pixel format to a format that is very much like that taught by Bayer in U.S. Pat. No. 3,971,065 for a color filter array for imaging devices for cameras. The advantage of the Martinez-Uriegas, et al. format is that it both captures and stores the individual color component data with similar spatial sampling frequencies as human vision. However, a first disadvantage is that the Martinez-Uriegas, et al format is not a good match for practical color display panels. For this reason, Martinez-Uriegas, et al. also teach how to convert the image back into RGB pixel format. Another disadvantage of the Martinez-Uriegas, et al. format is that one of the color components, in this case the red, is not regularly sampled. There are missing samples in the array, reducing the accuracy of the reconstruction of the image when displayed.
Full color perception is produced in the eye by three-color receptor nerve cell types called cones. The three types are sensitive to different wave lengths of light: long, medium, and short (“red”, “green”, and “blue”, respectively). The relative density of the three wavelengths differs significantly from one another. There are slightly more red receptors than green receptors. There are very few blue receptors compared to red or green receptors. In addition to the color receptors, there are relative wavelength insensitive receptors called rods that contribute to monochrome night vision.
The human vision system processes the information detected by the eye in several perceptual channels: luminance, chromanance, and motion. Motion is only important for flicker threshold to the imaging system designer. The luminance channel takes the input from only the red and green receptors. It is “color blind”. It processes the information in such a manner that the contrast of edges is enhanced. The chromanance channel does not have edge contrast enhancement. Since the luminance channel uses and enhances every red and green receptor, the resolution of the luminance channel is several times higher than the chromanance channel. The blue receptor contribution to luminance perception is negligible. Thus, the error introduced by lowering the blue resolution by one octave will be barely noticeable by the most perceptive viewer, if at all, as experiments at Xerox and NASA, Ames Research Center (R. Martin, J. Gille, J. Larimer, Detectability of Reduced Blue Pixel Count in Projection Displays, SID Digest 1993) have demonstrated.
Color perception is influenced by a process called “assimilation” or the Von Bezold color blending effect. This is what allows separate color pixels (or sub-pixels or emitters) of a display to be perceived as the mixed color. This blending effect happens over a given angular distance in the field of view. Because of the relatively scarce blue receptors, this blending happens over a greater angle for blue than for red or green. This distance is approximately 0.25.degree. for blue, while for red or green it is approximately 0.12.degree. At a viewing distance of twelve inches, 0.25.degree. subtends 50 mils (1,270.mu.) on a display. Thus, if the blue sub-pixel pitch is less than half (625.mu.) of this blending pitch, the colors will blend without loss of picture quality.
Sub-pixel rendering, in its most simplistic implementation, operates by using the sub-pixels as approximately equal brightness pixels perceived by the luminance channel. This allows the sub-pixels to serve as sampled image reconstruction points as opposed to using the combined sub-pixels as part of a ‘true’ pixel. By using sub-pixel rendering, the spatial sampling is increased, reducing the phase error.
If the color of the image were to be ignored, then each sub-pixel may serve as a though it were a monochrome pixel, each equal. However, as color is nearly always important (and why else would one use a color display?), then color balance of a given image is important at each location. Thus, the sub-pixel rendering algorithm must maintain color balance by ensuring that high spatial frequency information in the luminance component of the image to be rendered does not alias with the color sub-pixels to introduce color errors. The approaches taken by Benzschawel, et al. in U.S. Pat. No. 5,341,153, and Hill, et al. in U.S. Pat. No. 6,188,385, are similar to a common anti-aliasing technique that applies displaced decimation filters to each separate color component of a higher resolution virtual image. This ensures that the luminance information does not alias within each color channel.
If the arrangement of the sub-pixels were optimal for sub-pixel rendering, sub-pixel rendering would provide an increase in both spatial addressability to lower phase error and in Modulation Transfer Function (MTF) high spatial frequency resolution in both axes.
Examining the conventional RGB stripe display in FIG. 1, sub-pixel rendering will only be applicable in the horizontal axis. The blue sub-pixel is not perceived by the human luminance channel, and is therefore, not effective in sub-pixel rendering. Since only the red and green pixels are useful in sub-pixel rendering, the effective increase in addressability would be two-fold, in the horizontal axis. Vertical black and white lines must have the two dominant sub-pixels (i.e., red and green per each black or white line) in each row. This is the same number as is used in non-sub-pixel rendered images. The MTF, which is the ability to simultaneously display a given number of lines and spaces, is not enhanced by sub-pixel rendering. Thus, the conventional RGB stripe sub-pixel arrangement, as shown in FIG. 1, is not optimal for sub-pixel rendering.
The prior art arrangements of three-color pixel elements are shown to be both a poor match to human vision and to the generalized technique of sub-pixel rendering. Likewise, the prior art image formats and conversion methods are a poor match to both human vision and practicable color emitter arrangements.